

'''simple docstring'''





import math


class  UpperCAmelCase :

		'''simple docstring'''






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      0.0
							lowerCamelCase_  				=      0.0
							for i in range(len(SCREAMING_SNAKE_CASE_		)		):
												da += math.pow((sample[i] - weights[0][i])   ,			2		)
												da += math.pow((sample[i] - weights[1][i])   ,			2		)
												return 0 if da > da else 1
							return 0






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)  ->     list[list[int | float]]:






							'''simple docstring'''



							for i in range(len(SCREAMING_SNAKE_CASE_		)		):
												weights[j][i] += alpha * (sample[i] - weights[j][i])
							return weights







def   _UpperCamelCase	(		)       ->			None:
					# Training Examples ( m, n )
					lowerCamelCase_  				=      [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]

					# weight initialization ( n, C )
					lowerCamelCase_  				=      [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]

					# training
					lowerCamelCase_  				=      SelfOrganizingMap()
					lowerCamelCase_  				=      3
					lowerCamelCase_  				=      0.5

					for _ in range(__UpperCamelCase	):
										for j in range(len(__UpperCamelCase	)	):
															# training sample
															lowerCamelCase_  				=      training_samples[j]

															# Compute the winning vector
															lowerCamelCase_  				=      self_organizing_map.get_winner(__UpperCamelCase    ,__UpperCamelCase	)

															# Update the winning vector
															lowerCamelCase_  				=      self_organizing_map.update(__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase    ,__UpperCamelCase	)

    # classify test sample
					lowerCamelCase_  				=      [0, 0, 0, 1]
					lowerCamelCase_  				=      self_organizing_map.get_winner(__UpperCamelCase    ,__UpperCamelCase	)

					# results
					print(f'''Clusters that the test sample belongs to : {winner}'''	)
					print(f'''Weights that have been trained : {weights}'''	)


# running the main() function
if __name__ == "__main__":
				main()








#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes        # name
# #SBATCH --nodes=2                    # nodes
# #SBATCH --ntasks-per-node=1          # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10           # number of cores per tasks
# #SBATCH --gres=gpu:4                 # number of gpus
# #SBATCH --time 0:05:00               # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out           # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#

import fcntl
import os
import socket

import torch
import torch.distributed as dist







def  lowerCAmelCase_	(*lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			with open(lowerCAmelCase__		,						"""r"""  ) as fh:
						fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_EX  )
						try:
									print(*lowerCAmelCase__  )
						finally:
									fcntl.flock(lowerCAmelCase__		,						fcntl.LOCK_UN  )


a      :  Tuple			     =			int(os.environ['LOCAL_RANK'])
torch.cuda.set_device(local_rank)
a      :  Union[str, Any]			     =			torch.device('cuda', local_rank)
a      :  Any			     =			socket.gethostname()

a      :  Optional[Any]			     =			F'''[{hostname}-{local_rank}]'''

try:
	# test distributed
	dist.init_process_group('nccl')
	dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
	dist.barrier()

	# test cuda is available and can allocate memory
	torch.cuda.is_available()
	torch.ones(1).cuda(local_rank)

	# global rank
	a      :  Optional[Any]			     =			dist.get_rank()
	a      :  List[Any]			     =			dist.get_world_size()

	printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')

	dist.barrier()
	if rank == 0:
		printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')

except Exception:
	printflock(F'''{gpu} is broken''')
	raise






